no code implementations • 17 Nov 2023 • Barbaros Selnur Erdal, Vikash Gupta, Mutlu Demirer, Kim H. Fair, Richard D. White, Jeff Blair, Barbara Deichert, Laurie Lafleur, Ming Melvin Qin, David Bericat, Brad Genereaux
This paper reviews the challenges hindering the widespread adoption of artificial intelligence (AI) solutions in the healthcare industry, focusing on computer vision applications for medical imaging, and how interoperability and enterprise-grade scalability can be used to address these challenges.
no code implementations • 29 Dec 2022 • Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez, Ralf Floca, Laurence Jackson, Brad Genereaux, Sidney Bryson, Christopher P Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M. Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D. White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
no code implementations • 16 Feb 2022 • Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal
Traditional mammograms are being replaced by tomosynthesis and its other low radiation dose variants (for example Hologic' Intelligent 2D and C-View).
no code implementations • 28 Sep 2020 • Vikash Gupta, Clayton Taylor, Sarah Bonnet, Luciano M. Prevedello, Jeffrey Hawley, Richard D. White, Mona G. Flores, Barbaros Selnur Erdal
In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount.
no code implementations • 25 Sep 2020 • Vikash Gupta1, Holger Roth, Varun Buch3, Marcio A. B. C. Rockenbach, Richard D. White, Dong Yang, Olga Laur, Brian Ghoshhajra, Ittai Dayan, Daguang Xu, Mona G. Flores, Barbaros Selnur Erdal
The training of deep learning models typically requires extensive data, which are not readily available as large well-curated medical-image datasets for development of artificial intelligence (AI) models applied in Radiology.